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Related Concept Videos

Cluster Sampling Method01:20

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Multiple Targets CFAR Detection Performance Based on an Intelligent Clustering Algorithm in K-Distribution Sea

Mansoor M Al-Dabaa1, Eugen Laslo2, Ahmed A Emran1

  • 1Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

This study introduces Lin-DBSCAN-CFAR, an advanced detection scheme that improves Constant False Alarm Rate (CFAR) performance in complex sea clutter. The method effectively filters interfering targets, enhancing radar detection accuracy and efficiency in maritime environments.

Keywords:
K-distribution sea cluttercell under testlinear density-based spatial clustering for applications with noisemultiple targetssea clutter

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Area of Science:

  • Radar signal processing
  • Detection theory
  • Statistical signal processing

Background:

  • Maintaining a Constant False Alarm Rate (CFAR) is crucial for radar systems operating in dynamic maritime environments with K-distributed sea clutter.
  • Conventional CFAR detectors face performance limitations in multi-target scenarios due to the masking effect of interfering targets.
  • Sea clutter and interfering targets can manifest as outliers in radar data, complicating accurate target detection.

Purpose of the Study:

  • To develop an advanced CFAR detection scheme that overcomes the limitations of conventional methods in multi-target and complex sea clutter conditions.
  • To enhance the robustness and accuracy of radar detection by effectively isolating interfering targets and sea spikes.
  • To reduce the computational complexity of advanced CFAR techniques while maintaining high detection performance.

Main Methods:

  • Integration of Linear Density-Based Spatial Clustering for Applications with Noise (Lin-DBSCAN) with Constant False Alarm Rate (CFAR) processing.
  • Utilizing Lin-DBSCAN to identify and isolate interfering targets and sea spikes from the Cell Under Test (CUT) reference windows.
  • Comparative simulations evaluating the proposed Lin-DBSCAN-CFAR against conventional CFAR approaches and DBSCAN-CFAR.

Main Results:

  • The proposed Lin-DBSCAN-CFAR method demonstrates significantly improved detection accuracy and robustness compared to conventional CFAR techniques.
  • Lin-DBSCAN-CFAR effectively filters anomalous signals, enhancing performance in complex sea clutter and multi-target environments.
  • The method achieves detection performance comparable to the computationally intensive DBSCAN-CFAR but with substantially reduced complexity.

Conclusions:

  • Lin-DBSCAN-CFAR offers a superior and more efficient solution for radar target detection in challenging maritime environments.
  • The proposed scheme requires a lower Signal-to-Noise Ratio (SNR) for achieving desired detection probabilities, indicating enhanced sensitivity.
  • This advanced detection scheme provides a practical advancement for radar systems requiring reliable performance under varying sea clutter conditions.